Click here for interactive plots:

https://kforthman.shinyapps.io/COVID19_Interactive_Plots/

Click here to see how cities and counties overlap:

https://kforthman.shinyapps.io/500citiescounties

Compare COVID stats to Neighborhood Factors

data.cor <- cor(county.Demo_and_Covid.allcounties[,-1], use = "complete.obs", method = "spearman")
corrplot.mixed(data.cor, upper = 'ellipse', lower = 'number', tl.pos = 'lt', tl.cex = 1, lower.col = "black", number.cex = 0.5)

Compare COVID stats to 500 cities data and Neighborhood Factors

data.cor2 <- cor(county.Demo_and_Covid.500counties[,-c(1:2)], use = "complete.obs", method = "spearman")
corrplot.mixed(data.cor2, upper = 'ellipse', lower = 'number', tl.pos = 'lt', tl.cex = 1, lower.col = "black", number.cex = 0.5)

corrplot.mixed(data.cor2[7:13,c(1:5, 14:42,6)], upper = 'ellipse', tl.pos = 'lt', tl.cex = 1, lower.col = "black", number.cex = 0.5)

—-Linear Mixed Effects Model —-

this.lme <- lmer("total.cases.percap ~ Affluence + Singletons.in.Tract + Seniors.in.Tract + African.Americans.in.Tract + Noncitizens.in.Tract + High.BP + Binge.Drinking + Cancer + Asthma + Heart.Disease + COPD + Smoking + Diabetes + No.Physical.Activity + Obesity + Poor.Sleeping.Habits + Poor.Mental.Health + Testing_Rate + Hospitalization_Rate + (1 | stateID)", data = county.Demo_and_Covid.500counties)
## Warning: Some predictor variables are on very different scales: consider
## rescaling

## Warning: Some predictor variables are on very different scales: consider
## rescaling
print(summary(this.lme), correlation=TRUE)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## "total.cases.percap ~ Affluence + Singletons.in.Tract + Seniors.in.Tract + African.Americans.in.Tract + Noncitizens.in.Tract + High.BP + Binge.Drinking + Cancer + Asthma + Heart.Disease + COPD + Smoking + Diabetes + No.Physical.Activity + Obesity + Poor.Sleeping.Habits + Poor.Mental.Health + Testing_Rate + Hospitalization_Rate + (1 | stateID)"
##    Data: county.Demo_and_Covid.500counties
## 
## REML criterion at convergence: -1136.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.7838 -0.3381 -0.0799  0.1946  5.7385 
## 
## Random effects:
##  Groups   Name        Variance     Std.Dev. 
##  stateID  (Intercept) 0.0000007025 0.0008381
##  Residual             0.0000128022 0.0035780
## Number of obs: 169, groups:  stateID, 32
## 
## Fixed effects:
##                                  Estimate     Std. Error             df
## (Intercept)                 -0.0078834524   0.0091117091  62.9937978090
## Affluence                    0.0044273771   0.0010548742  89.8915250462
## Singletons.in.Tract          0.0016756747   0.0009199923 130.1898431447
## Seniors.in.Tract             0.0009573458   0.0011968482 141.0275922385
## African.Americans.in.Tract   0.0001892384   0.0010033390 142.8234576507
## Noncitizens.in.Tract         0.0008252479   0.0007412363 114.4679044712
## High.BP                      0.0002290670   0.0001834995  84.9283261346
## Binge.Drinking               0.0001342278   0.0001471676  34.0140216879
## Cancer                      -0.0008074457   0.0010558317  82.9404698028
## Asthma                       0.0004799810   0.0005131528  31.6148543958
## Heart.Disease                0.0006228789   0.0012304569  60.2513164164
## COPD                         0.0000114929   0.0010388888  63.7611319806
## Smoking                     -0.0001376212   0.0002196774  67.7217850678
## Diabetes                    -0.0004584243   0.0005193751  64.7329511547
## No.Physical.Activity         0.0000023663   0.0001960089  74.2642571562
## Obesity                      0.0001982331   0.0001686597  78.2031386556
## Poor.Sleeping.Habits        -0.0000034336   0.0001626057 116.3049103396
## Poor.Mental.Health          -0.0000195159   0.0003872136  26.1416586492
## Testing_Rate                 0.0000005056   0.0000003146  28.4167113763
## Hospitalization_Rate        -0.0001238669   0.0000840245  24.1970262355
##                            t value  Pr(>|t|)    
## (Intercept)                 -0.865    0.3902    
## Affluence                    4.197 0.0000634 ***
## Singletons.in.Tract          1.821    0.0708 .  
## Seniors.in.Tract             0.800    0.4251    
## African.Americans.in.Tract   0.189    0.8507    
## Noncitizens.in.Tract         1.113    0.2679    
## High.BP                      1.248    0.2153    
## Binge.Drinking               0.912    0.3681    
## Cancer                      -0.765    0.4466    
## Asthma                       0.935    0.3567    
## Heart.Disease                0.506    0.6146    
## COPD                         0.011    0.9912    
## Smoking                     -0.626    0.5331    
## Diabetes                    -0.883    0.3807    
## No.Physical.Activity         0.012    0.9904    
## Obesity                      1.175    0.2434    
## Poor.Sleeping.Habits        -0.021    0.9832    
## Poor.Mental.Health          -0.050    0.9602    
## Testing_Rate                 1.607    0.1190    
## Hospitalization_Rate        -1.474    0.1533    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of fixed effects could have been required in summary()
## 
## Correlation of Fixed Effects:
##             (Intr) Afflnc Sng..T Snr..T A.A..T Nnc..T Hgh.BP Bng.Dr Cancer
## Affluence    0.157                                                        
## Sngltns.n.T -0.005  0.049                                                 
## Snrs.n.Trct  0.591  0.378  0.172                                          
## Afrcn.Am..T  0.191  0.160 -0.435  0.168                                   
## Nnctzns.n.T -0.010  0.094  0.048  0.060 -0.075                            
## High.BP      0.019  0.245  0.094  0.134 -0.110  0.397                     
## Bing.Drnkng -0.240 -0.181 -0.302 -0.168  0.115  0.054  0.145              
## Cancer      -0.596 -0.213  0.180 -0.344 -0.078 -0.156 -0.400 -0.135       
## Asthma      -0.354 -0.212 -0.205 -0.160  0.080  0.089  0.169 -0.019  0.037
## Heart.Dises -0.147  0.078 -0.284 -0.150  0.237 -0.097 -0.032  0.062 -0.460
## COPD         0.552  0.037  0.132  0.270  0.007  0.289  0.215  0.126 -0.266
## Smoking     -0.192  0.104 -0.179 -0.133 -0.087 -0.009 -0.107 -0.298  0.087
## Diabetes     0.057 -0.308 -0.159 -0.233 -0.274 -0.327 -0.525  0.040  0.222
## N.Physcl.Ac -0.176 -0.069  0.100 -0.028 -0.030 -0.229 -0.121  0.082  0.494
## Obesity      0.030  0.439  0.393  0.309  0.164  0.215 -0.069 -0.218  0.103
## Pr.Slpng.Hb -0.501 -0.415  0.180 -0.393 -0.407  0.007 -0.185  0.061  0.175
## Pr.Mntl.Hlt -0.316  0.262 -0.052 -0.060  0.111 -0.202 -0.097  0.040  0.310
## Testing_Rat  0.188 -0.092 -0.082 -0.011  0.057 -0.100 -0.015  0.033 -0.189
## Hsptlztn_Rt -0.139 -0.219 -0.161 -0.271 -0.071 -0.129 -0.133 -0.166  0.060
##             Asthma Hrt.Ds COPD   Smokng Diabts N.Ph.A Obesty Pr.S.H Pr.M.H
## Affluence                                                                 
## Sngltns.n.T                                                               
## Snrs.n.Trct                                                               
## Afrcn.Am..T                                                               
## Nnctzns.n.T                                                               
## High.BP                                                                   
## Bing.Drnkng                                                               
## Cancer                                                                    
## Asthma                                                                    
## Heart.Dises  0.275                                                        
## COPD        -0.364 -0.565                                                 
## Smoking      0.072  0.220 -0.527                                          
## Diabetes    -0.123 -0.236 -0.162  0.291                                   
## N.Physcl.Ac  0.012 -0.396  0.002 -0.349 -0.087                            
## Obesity     -0.288 -0.109  0.190 -0.219 -0.400 -0.059                     
## Pr.Slpng.Hb  0.084  0.244 -0.218  0.043 -0.017 -0.099 -0.175              
## Pr.Mntl.Hlt -0.222  0.094 -0.458  0.079  0.036  0.079  0.093 -0.193       
## Testing_Rat -0.359 -0.022  0.181  0.183  0.150 -0.333  0.074 -0.138 -0.122
## Hsptlztn_Rt  0.046  0.077 -0.107  0.152  0.118 -0.047 -0.134  0.023 -0.039
##             Tstn_R
## Affluence         
## Sngltns.n.T       
## Snrs.n.Trct       
## Afrcn.Am..T       
## Nnctzns.n.T       
## High.BP           
## Bing.Drnkng       
## Cancer            
## Asthma            
## Heart.Dises       
## COPD              
## Smoking           
## Diabetes          
## N.Physcl.Ac       
## Obesity           
## Pr.Slpng.Hb       
## Pr.Mntl.Hlt       
## Testing_Rat       
## Hsptlztn_Rt  0.270
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling

Testing Rate

testing.data.state <- compiled.stats[[length(daily_filenames)]][, c("Province_State", "Testing_Rate")]
testing.data.state <- testing.data.state[!is.na(testing.data.state$Testing_Rate),]
testing.data.state <- testing.data.state[order(testing.data.state$Testing_Rate),]

col.state <- rep("pink", nrow(testing.data.state))

avg.test.rate <- mean(testing.data.state$Testing_Rate, na.rm = T)

col.state[testing.data.state$Testing_Rate < avg.test.rate] <- "grey"
col.state[testing.data.state$Province_State == "Oklahoma"] <- "lightblue"

par(mar = c(5,6,4,2))
barplot(testing.data.state$Testing_Rate, names.arg = testing.data.state$Province_State, horiz = T, main = "Testing Rate by State", las = 2, cex.axis = 1, cex.names = 0.5, col = col.state, border = F, xlab = "Total number of people tested per 100,000 persons.")
abline(v = avg.test.rate, col = "red")
text(x = avg.test.rate + 10, y = 1, labels = "Average Testing Rate", adj = c(0, 0.5), col = "red")

day.first.case <- min(which(US.total$cases.total > 100))
n.days <- nrow(US.total)

par(mar = c(5,5,4,2))
barplot(US.total$cases.total[day.first.case:n.days], main = "Total COVID-19 cases by Date in US", las = 2, cex.axis = 1, cex.names = 0.5)

barplot(US.total$cases.total[day.first.case:n.days], main = "Total COVID-19 cases by Date in US, log scale", las = 2, cex.axis = 1, cex.names = 0.5, log = "y")

barplot(US.total$deaths.total[day.first.case:n.days], main = "Total COVID-19 deaths by Date in US", las = 2, cex.axis = 1, cex.names = 0.5)

barplot(US.total$deaths.total[day.first.case:n.days], main = "Total COVID-19 deaths by Date in US, log scale", las = 2, cex.axis = 1, cex.names = 0.5, log = "y")

barplot(US.total$rise.cases.total[day.first.case:n.days], main = "Rise in Cases of COVID-19 by Date in US", las = 2, cex.axis = 1, cex.names = 0.5)

barplot(US.total$rise.deaths.total[day.first.case:n.days], main = "Rise in Deaths of COVID-19 by Date in US", las = 2, cex.axis = 1, cex.names = 0.5)